PROJECT SUMMARY/ABSTRACT (DESCRIPTION) Cigarette smoking accounts for 480,000 premature deaths and one third of all cancer deaths annually in the US. There is enormous need for high-impact, cost-effective population-level interventions for smoking cessation. For the past 15 years, mobile phone-delivered text messaging interventions such as the NCI?s SmokefreeTXT have been a prominent technology addressing this need. However, very much like all widely available technologies for smoking cessation (e.g., websites), text messaging interventions have modest quit rates, driven largely by low engagement. Fortunately, a new technology provides a therapeutic conversation to address the problem of engagement that impacts text messaging and other current digital technologies for smoking cessation. Advances in machine learning, natural language processing, and cloud computing are now making it possible to create and widely disseminate conversational agents (CAs), which are computer-powered digital coaches designed to form long-term social-emotional connections with users through conversations. CAs are supportive, empathic, reflectively listen, provide personalized responses, and offer goal setting and advice appropriately timed to the needs of the user. Regarding CAs for smoking cessation, the major knowledge gaps are: (1) their efficacy, (2) theoretical mechanisms, and (3) the cost-effectiveness. Also unexplored are the potential baseline moderators of CAs for smoking cessation. We recently developed a CA for smoking cessation, called ?QuitBot,? evaluated it in a diary study, and then tested it in a pilot randomized controlled trial (N = 306), comparing it with the NCI?s SmokefreeTXT. The pilot RCT design was very feasible with 93% three-month follow-up. QuitBot had: (a) high participant engagement and (b) high quit rates at the three-month follow-up?very promising in comparison with SmokefreeTXT. Addressing these knowledge gaps and building on the promising results of our QuitBot research, the project will conduct a randomized controlled trial of QuitBot (n = 760) versus SmokefreeTXT (n = 760) with 12-month follow-up in order to determine whether QuitBot: (1) provides higher quit rates than SmokefreeTXT, (2) has smoking cessation outcomes significantly mediated by therapeutic alliance processes and engagement, and (3) is cost-effective vs. SmokefreeTXT. In addition, this study will explore whether these baseline factors moderate the effectiveness of QuitBot: trust, social support, and demographics (e.g., sex). This innovative project will advance the fields of research on CAs both for smoking cessation in particular and for health behavior change in general? regardless of whether the results are positive or null. Positive results could have high population-level impact and stimulate new lines of research into CA dissemination and implementation, and the adaptation of CAs for multiple subpopulations of smokers, languages, and community and medical settings.